add langchain support (#1805)
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LawyZheng
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integrations/langchain/README.md
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integrations/langchain/README.md
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<!-- START doctoc generated TOC please keep comment here to allow auto update -->
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<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
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**Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)*
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- [Skyvern Langchain](#skyvern-langchain)
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- [Installation](#installation)
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- [Usage](#usage)
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- [Run a task(sync) with skyvern agent (calling skyvern agent function directly in the tool)](#run-a-tasksync-with-skyvern-agent-calling-skyvern-agent-function-directly-in-the-tool)
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- [Run a task(async) with skyvern agent (calling skyvern agent function directly in the tool)](#run-a-taskasync-with-skyvern-agent-calling-skyvern-agent-function-directly-in-the-tool)
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- [Run a task(sync) with skyvern client (calling skyvern OpenAPI in the tool)](#run-a-tasksync-with-skyvern-client-calling-skyvern-openapi-in-the-tool)
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- [Run a task(async) with skyvern client (calling skyvern OpenAPI in the tool)](#run-a-taskasync-with-skyvern-client-calling-skyvern-openapi-in-the-tool)
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<!-- END doctoc generated TOC please keep comment here to allow auto update -->
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# Skyvern Langchain
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This is a langchain integration for Skyvern.
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## Installation
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```bash
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pip install skyvern-langchain
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```
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## Usage
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### Run a task(sync) with skyvern agent (calling skyvern agent function directly in the tool)
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> sync task won't return until the task is finished.
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:warning: :warning: if you want to run this code block, you need to run `skyvern init --openai-api-key <your_openai_api_key>` command in your terminal to set up skyvern first.
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```python
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import asyncio
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langchain.agents import initialize_agent, AgentType
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from skyvern_langchain.agent import run_task_v2
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load_dotenv()
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llm = ChatOpenAI(model="gpt-4o", temperature=0)
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agent = initialize_agent(
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llm=llm,
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tools=[run_task_v2],
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verbose=True,
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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)
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async def main():
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# to run skyvern agent locally, must run `skyvern init` first
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print(await agent.ainvoke("Run a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.'"))
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if __name__ == "__main__":
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asyncio.run(main())
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```
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### Run a task(async) with skyvern agent (calling skyvern agent function directly in the tool)
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> async task will return immediately and the task will be running in the background. You can use `get_task_v2` tool to poll the task information until the task is finished.
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:warning: :warning: if you want to run this code block, you need to run `skyvern init --openai-api-key <your_openai_api_key>` command in your terminal to set up skyvern first.
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```python
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import asyncio
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langchain.agents import initialize_agent, AgentType
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from skyvern_langchain.agent import queue_task_v2, get_task_v2
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from langchain_community.tools.sleep.tool import SleepTool
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load_dotenv()
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llm = ChatOpenAI(model="gpt-4o", temperature=0)
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agent = initialize_agent(
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llm=llm,
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tools=[
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queue_task_v2,
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get_task_v2,
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SleepTool(),
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],
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verbose=True,
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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)
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async def main():
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# use sleep tool to set up the polling logic until the task is completed, if you only want to queue a task, you can remove the sleep tool
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print(await agent.ainvoke("Queue a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.' Then, get this task information until it's completed. The task information re-get interval should be 60s."))
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if __name__ == "__main__":
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asyncio.run(main())
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```
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### Run a task(sync) with skyvern client (calling skyvern OpenAPI in the tool)
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> sync task won't return until the task is finished.
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no need to run `skyvern init` command in your terminal to set up skyvern before using this integration.
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```python
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import asyncio
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langchain.agents import initialize_agent, AgentType
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from skyvern_langchain.client import RunSkyvernClientTaskV2Tool
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load_dotenv()
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llm = ChatOpenAI(model="gpt-4o", temperature=0)
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run_task_v2 = RunSkyvernClientTaskV2Tool(
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credential="<your_organization_api_key>",
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)
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agent = initialize_agent(
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llm=llm,
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tools=[run_task_v2],
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verbose=True,
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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)
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async def main():
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print(await agent.ainvoke("Run a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.'"))
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if __name__ == "__main__":
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asyncio.run(main())
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```
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### Run a task(async) with skyvern client (calling skyvern OpenAPI in the tool)
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> async task will return immediately and the task will be running in the background. You can use `GetSkyvernClientTaskV2Tool` tool to poll the task information until the task is finished.
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no need to run `skyvern init` command in your terminal to set up skyvern before using this integration.
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```python
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import asyncio
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langchain.agents import initialize_agent, AgentType
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from skyvern_langchain.client import (
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QueueSkyvernClientTaskV2Tool,
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GetSkyvernClientTaskV2Tool,
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)
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from langchain_community.tools.sleep.tool import SleepTool
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load_dotenv()
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llm = ChatOpenAI(model="gpt-4o", temperature=0)
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queue_task_v2 = QueueSkyvernClientTaskV2Tool(
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credential="<your_organization_api_key>",
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)
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get_task_v2 = GetSkyvernClientTaskV2Tool(
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credential="<your_organization_api_key>",
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)
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agent = initialize_agent(
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llm=llm,
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tools=[
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queue_task_v2,
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get_task_v2,
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SleepTool(),
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],
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verbose=True,
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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)
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async def main():
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# use sleep tool to set up the polling logic until the task is completed, if you only want to queue a task, you can remove the sleep tool
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print(await agent.ainvoke("Queue a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.' Then, get this task information until it's completed. The task information re-get interval should be 60s."))
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if __name__ == "__main__":
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asyncio.run(main())
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```
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